Welcome to the SalaryPredictorML repository by Raj Vidja. This project uses advanced machine learning to redefine salary determination for new hires at TechWorks Consulting. Consideration of factors like education, experience, role, previous compensation, and academic records ensures fair and competitive salary predictions.
- Predictive model for employee salary determination.
- Utilizes machine learning algorithms to analyze diverse variables.
- Enhances objectivity in salary decisions for new hires.
- Evaluates and optimizes model performance for accuracy.
- Clone the repository to your local machine.
- Install the required dependencies listed in the "Install Necessary Modules" section.
- Follow the Jupyter notebooks for training and evaluation.
- Explore the model's predictions and insights.
Google Colab Project Link : https://colab.research.google.com/drive/1g0vmMjp7B-eLD1dg8OzWL7luq3DXtRnP
Open your 
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pip install numpy -
pip install pandas -
pip install seaborn -
pip install matplotlib -
pip install statsmodels -
pip install scikit-learn -
pip install pydotplus -
pip install xgboost -
pip install tabulate -
pip install colorama -
pip install gradio
Once Installed now we can import it inside our python code.
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Go
hereand click the ➞Codebutton in the top right of the page, then click ➞Download ZIP.
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Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.
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Launch ipython notebook from the folder which contains the notebooks. Open each one of them
Kernel > Restart & Clear Output
This will clear all the outputs and now you can understand each statement and learn interactively.
If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.
I'm Raj Vidja and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome🙏
See github's contributors page for details.
If you have trouble with this tutorial please tell me about it by Create an issue on GitHub. and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.
If you like this tutorial, please give it a ⭐ star.
You may use this tutorial freely at your own risk. See LICENSE.
Copyright (c) 2023 Raj Vidja
